loess_optnum: determine optimal spatial data discretization for individual...

In sdsfun: Spatial Data Science Complementary Features

determine optimal spatial data discretization for individual variables

Description

Function for determining optimal spatial data discretization for individual variables based on locally estimated scatterplot smoothing (LOESS) model.

Usage

loess_optnum(qvec, discnumvec, increase_rate = 0.05)

Arguments

qvec	A numeric vector of q statistics.
discnumvec	A numeric vector of break numbers corresponding to qvec.
increase_rate	(optional) The critical increase rate of the number of discretization. Default is 0.05.

Value

A two element numeric vector.

discnum

optimal number of spatial data discretization

increase_rate

the critical increase rate of the number of discretization

Note

When increase_rate is not satisfied by the calculation, the discrete number corresponding to the highest ⁠q statistic⁠ is selected as a return.

Note that sdsfun sorts discnumvec from smallest to largest and keeps qvec in one-to-one correspondence with discnumvec.

Examples

qv = c(0.26045642,0.64120405,0.43938704,0.95165535,0.46347836,
       0.25385338,0.78778726,0.95938330,0.83247885,0.09285196)
loess_optnum(qv,3:12)

sdsfun documentation built on June 8, 2025, 10:39 a.m.